Overlay is one of the most critical design parameters in integrated circuit manufacturing. Maintaining good overlay performance during manufacturing is therefore essential in order to obtain high yield and to ensure that the performance and reliability of the eventual semiconductor device is according to specifications. For that reason, optical metrology is nowadays extensively used in any production facility for overlay monitoring and process control. Overlay metrology is typically required after each lithography step for (nearly) every lot. The number of process and lithograpy steps have increased significantly with advancing technology nodes and consequently there is an increased demand for overlay metrology. Although the benefits of overlay metrology are obvious, the use of metrology should be kept at acceptable levels as it adds cost and increases fab cycle time. Virtual overlay metrology, the replacement of some real overlay measurements with predicted values, is an effective solution for keeping the need for overlay metrology under control.
Overlay is a one of the most critical design specifications in semiconductor device manufacturing. Any state-of- the-art production facility has overlay metrology in place to monitor overlay performance during manufacturing and to use the measurements for overlay control. Especially since the introduction of multi-patterning, with its tight overlay requirements and increased number of process steps, there has been an increased need for additional metrology. Overlay metrology brings cost-added value to semiconductor device manufacturing and it should be reduced to a minimum to keep costs at acceptable levels, which can be a challenge in the multi-patterning era. Replacing some real overlay measurements with predicted values, referred to as virtual overlay metrology, could be a viable solution to address this challenge. In this work, we develop virtual overlay metrology and aim at predicting the overlay for a series of implant layers. To this end, we apply machine learning algorithms, and neural networks in particular, to build a complex non-linear model directly from data. Our model takes a set of features that are designed based on the physical concepts of overlay and outputs the overlay map of a target layer. The features include overlay of another implant layer of the same wafer, exposure tool fingerprints, scanner logging, and process data. We evaluate our model using production data and we show the prediction performance for the raw overlay, as well as for the correctable and non-correctable overlay errors.
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